Triple
T2968909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C. K. Ogden |
E80232
|
entity |
| Predicate | numberOfWordsInBasicEnglishCore |
P7605
|
FINISHED |
| Object | 850 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 850 | Statement: [C. K. Ogden, numberOfWordsInBasicEnglishCore, 850]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWordsInBasicEnglishCore Context triple: [C. K. Ogden, numberOfWordsInBasicEnglishCore, 850]
-
A.
estimatedNumberOfLanguages
Indicates the approximate count of distinct languages associated with an entity, typically based on estimation rather than an exact measurement.
-
B.
wordCount
chosen
Indicates the total number of words contained in a given text or linguistic unit.
-
C.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
D.
hasApproximateNumberOfLanguages
Indicates that an entity is associated with a quantity representing an estimated or non-exact count of languages.
-
E.
hasRootWord
Indicates that one linguistic form is derived from, based on, or directly associated with a specified root word.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad8b14ffe881908ffed62f9595c867 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9970e2c08190affa5efedb9aad75 |
completed | March 8, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69ad960e71f8819088179d11248c6ed0 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:58 p.m.